Advanced information technology solutions for implementing information sharing across supply chains

Marinagi, Catherine; Skourlas, Christos; Galiotou, Eleni

doi:10.1145/3291533.3291575

Cited by 10 publications

(20 citation statements)

References 25 publications

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“…Fatorachian and Kazemi (2018) discussed that in order to enable extensive connectivity within current advanced manufacturing companies, manufacturing companies need to use a broader scope of advanced and computer integrated technologies. Marinagi et al (2018) predict that advanced integrated Information Technologies (IT) can be exploited for information sharing across supply chains, enabling collaboration and interoperability. Computers play a key role in manufacturing digitalization, but resulting in so-called data silos or isolated data islands (Cohen et al , 2019).…”

Section: Results Findingsmentioning

confidence: 99%

“…Companies can move as much or as little functionality as they want to the cloud, making specialized business processes available across the Internet or the company Intranet. Marinagi et al (2018) discussed that cloud computing can process, analyse and store supply chain big data and meet the requirements of SCM which needs efficient management of large The main drawback of such a solution is that the experimental evaluation is limited to the simulations of RFID, without considering the whole conceived architecture, which also consists of private and public cloud, and other service provider modules (e.g. a security management and authorization module).…”

Section: Supply Chain Integration Andmentioning

confidence: 99%

“…Manavalan and Jayakrishna (2019) discusses that The "Unique Sense" implementation model of Industry 4.0 also known as the fourth industrial revolution for industrial system holds the innovative smart computing focuses towards dynamic customization, reusability, eco-friendliness for next generation controlling and computation power. Marinagi et al (2018) in his article suggested that Industry 4.0 is a promising technology but it is still under development phase, which is expected to affect SCM activities and create business value by enabling mass customization, introducing new products and services, meeting customers' demands and optimizing decision-making process. The multi-layer IoT system can be targeted to reach an optimal balance of efficiency and flexibility that help reduce cost and increase customization (Salam, 2019).…”

Section: Supply Chain Integration Andmentioning

confidence: 99%

“…(2018),Manavalan and Jayakrishna (2019),Marinagi et al (2018,Raptis et al (2019),Bicocchi et al (2019),Qiu et al (2018),Factorachian and Kazemi (2020),Tuptuk and Hailes (2018),Kang et al (2016), Monostori et al (2016), Cohen et al (2019) M2: Human factors Pinzone et al (2020), Li (2018), Cardin (2019), Da Silva et al (2019), Piccarozzi et al (2018), Winkelhaus and Grosse (2020), Klumpp et al (2019), Buchi et al (2020), Qu et al (2019), Yang et al (2019), Singh et al (2019) M3: cyber-physical industrial system Cimini et al (2020), Monostori et al (2016), Thoben et al (2017), Rossit et al (2019a, b, c), Pinzone et al (2020), Khakifirooz et al (2018), Cardin (2019), Schuh et al (2014), Hohmann and Posselt (2019), Rojas and Rauch (2019), Muhuri et al (2019), Romero-Silva and Hern andez-L opez (2020), Kusiak (2018), Juh asz and B anyai (2018) M4: Quality management/ Maintenance Foidl and Felderer (2015), Ben-Daya et al (2019), Turner et al (2019), Singh et al (2019), Cohen et al (2019) Logistics(L) L1: Warehousing Singh et al (2019), Ben-Daya et al (2019), Winkelhaus and Grosse (2020). Gru zauskas et al (2018) L2: Procurement/ Purchases Factorachian and Kazemi (2020), Bag et al (2020) L3: Distribution/ Routing/ Transportation Heged} us et al (2019), Hohmann and Posselt (2019), Oztemel and Gursev (2020), Ben-Daya et al (2019), Winkelhaus and Grosse (2020), Gru zauskas et al (2018), Kusiak (2018) Design/ Development(D) D1: Collaborative design/Resource Allocation Manavalan and Jayakrishna (2019), Khakifirooz et al (2018), Bicocchi et al (2019), Kerin and Pham (2019) Returned product(R) R1: Recycle/reuse Garrido-Hidalgo et al (2019), Kerin and Pham (2019), Manavalan and Jayakrishna (2019) Industry 4Kerin and Pham (2019), Khakifirooz et al (2018), Marinagi et al (2018), Garrido-Hidalgo et al (2019), Hohmann and Posselt (2019), Sicari et al (2019), Winkelhaus and Grosse (2020), Bag et al (2020), Da Silva et al (2019) InQ2: Multicloud O'Donovan et al (2019), H€ ackel et al (2019), Heged} us et al (2019) Intraorganizational(IQ) IQ1: RFID þ ERP Yang et al (2019), Qu et al (2019), Kang et al (2016), Foidl and Felderer (2015), Cui et al (2020), Monostori et al (2016), Manavalan and Jayakrishna (2019) IQ2: Cloud þ ERP Fatorachian and Kazemi (2018), Rossit et al (2019a), Choi et al (2017), Ben-Daya et al (2019) (continued )…”

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confidence: 99%

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Supply chain integration and Industry 4.0: a systematic literature review

Tiwari

2020

BIJ

126

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PurposeThe current industrial revolution is powered by data, which is also referred as Industry 4.0. The Industry 4.0 has attracted significant attention from academia and the industry professionals. The supply chain integration (SCI) has played a significant role in enhancing supply chain performance and organizational performance. This study explores the relationship between Industry 4.0 and SCI via an extensive literature review to understand the various levels of integration with the supply chain processes and to identify missing links, through a framework, and suggest further research directions.Design/methodology/approachIn this paper, we have used systematic literature review approach to identify the building blocks of the conceptual framework, which is the main contribution of the present study. We have used Scopus database to search literature using keywords.FindingsThe study offers some interesting insights that may help scholars to advance theoretical debates. Moreover, the study also provides interesting direction to the practitioners engaged in supply chain management in data-driven environment. In this study, we have proposed a conceptual framework for the adoption of Industry 4.0 and SCI.Research limitations/implicationsIn this study we have proposed a conceptual framework. However, the framework is yet to be empirically tested. Hence, we caution readers to evaluate the findings of the present study in context to its limitations. This is an attempt to develop a conceptual framework which may be tested using longitudinal data.Originality/valueThe present work helps in integrating two independent subjects', i.e. Industry 4.0 and SCI. The theoretical framework presented here integrates Industry 4.0 and SCI which can be useful to the practitioners and policymakers engaged in implementing Industry 4.0.

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Section: Results Findingsmentioning

confidence: 99%

Section: Supply Chain Integration Andmentioning

confidence: 99%

Section: Supply Chain Integration Andmentioning

confidence: 99%

mentioning

confidence: 99%

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Supply chain integration and Industry 4.0: a systematic literature review

Tiwari

2020

BIJ

126

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“…Furthermore, the use of IoT and big data in the supply chain helps in understanding and accessing customer demand in real-time, enabling organizations to develop product quality and design (Kerin and Pham, 2019). These technologies allow companies to respond immediately to customers' requirements, build solid relationships, collaborate with supply chain partners, improve the capability to real-time forecasting, and monitor threats in the supply chain (Marinagi et al, 2018). Once the organization integrates the supply chain with Industry 4.0 technologies, it offers numerous advantages, including agility, versatility, efficiency, and accessibility in the supply chain management, improving the firms' competencies (Winkelhaus and Grosse, 2020).…”

Section: Supply Chain Integrationmentioning

confidence: 99%

Industry 4.0 implementation and Triple Bottom Line sustainability: An empirical study on small and medium manufacturing firms

Jayashree

Reza

Malarvizhi

et al. 2021

Heliyon

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Background The current level of industrialization has generated many challenges worldwide, including ecological hazards, climate change, and the overuse of non-renewable natural resources, thereby creating an increasing demand for achieving the goal of the Triple Bottom Line (TBL). In this regard, Industry 4.0 can be used as a crunch point to contribute to the production process that can help achieve sustainable development. Purpose While the Malaysian government proposed the “Industry4ward” approach to enhance technological adoption, there is scarce empirical evidence in the literature that validates SMEs for Industry 4.0. Using Dynamic Capability View (DCV), this study proposes a framework that includes core determinants like top management commitment, supply chain integration, and IT infrastructure, that can significantly influence Industry 4.0 implementation toward achieving TBL sustainability. Design/methodology/approach Employing simple random sampling, the study adopted a quantitative approach based on 199 useable respondent's feedback collected through a survey questionnaire of 900 employees from Malaysian SMEs. The statistical analysis was performed using Structural Equation Modeling (Partial Least Square, SmartPLS 3.3.2). Findings The results show that top management and IT infrastructure significantly impact Industry 4.0 implementation and sustainability. In contrast, the analysis also demonstrates that supply chain integration is insignificant to Industry 4.0 implementation in SMEs. The findings also indicate that the relationship between the determinants of Industry 4.0 and TBL sustainability can be mediated by the “effective implementation” of Industry 4.0. Recommendations The study highlights the practical consequences of the role and use of the determinants in Industry 4.0 implementation. Its findings help managers and policy-makers to optimize value creation to achieve sustainable development goals. Limitations and future research Focusing only on Malaysian manufacturing SMEs may restrict the generalization of the study; thus, a benchmarking analysis from other industrial settings is encouraged. The questionnaire-based survey is a further limitation of the study.

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